What Is an LLM Token Counter?
An LLM token counter is a free online tool that tells you exactly how many tokensa piece of text uses before you send it to a large language model. Models like GPT-5, GPT-4o, Claude, Gemini, Llama and DeepSeek don't read words — they read tokens, the small chunks their tokenizer breaks text into. Because both context limits and API billing are measured in tokens, counting them first lets you control cost, avoid truncation, and optimize your prompts with confidence.
This counter uses OpenAI's official tiktoken encodings (o200k_base and cl100k_base) for exact GPT and o-series counts, and calibrated estimates for providers that don't publish a browser tokenizer. Everything runs locally in your browser — nothing is uploaded.
How to Use the Token Counter
- Add your text. Paste a prompt, open a
.txt,.mdor code file with Open File, or load the sample. - Pick a model. Choose from GPT-5, GPT-4o, Claude, Gemini, Llama and more — token counts update in real time.
- Read the metrics.See total tokens, characters, words, tokens-per-word, and how much of the model's context window you're using.
- Estimate cost. Enter your expected output tokens to get a per-request price, then compare every model side by side.
- Visualize tokens. Toggle Visualize to see exactly where each token boundary falls in your text.
Why Use Our Token Counter
- Exact OpenAI counts via the real tiktoken BPE encodings — not a rough character guess.
- Built-in cost calculator with separate input and output pricing across 18+ models.
- Side-by-side model comparison so you can pick the cheapest model that fits your prompt.
- Context-window meter that warns you before you hit a limit.
- 100% private & offline-capable — your text never leaves the browser, with no sign-up and no limits.
Common Use Cases
- Prompt engineering: trim and refine prompts to fit context windows and reduce spend.
- Cost budgeting: forecast API bills before shipping a feature to production.
- RAG & chunking:size document chunks so they fit a model's window with room for the answer.
- Model selection: compare token efficiency and price between GPT, Claude and Gemini for the same content.
- Fine-tuning & datasets: estimate training and inference token volumes for large text corpora.
Exact vs. Estimated Token Counts
For all OpenAI models we run the exact o200k_base (GPT-5, GPT-4.1, GPT-4o, o-series) and cl100k_base (GPT-4 Turbo, GPT-3.5) encodings, so results match the API to the token. Anthropic, Google, Meta, DeepSeek, xAI and Mistral do not ship public browser tokenizers, so their counts are close approximations using calibrated character-per-token ratios — typically within a few percent for English text. Counts marked EXACT are authoritative; those marked ESTIMATE are a reliable guide for budgeting.
Frequently Asked Questions
What is an LLM token?
A token is the basic unit a language model reads and generates. Tokens can be whole words, parts of words, punctuation, or single characters. As a rough guide, 1,000 tokens is about 750 English words, but the exact number depends on the model's tokenizer and the language you use.
How accurate is this token counter?
For OpenAI models (GPT-5, GPT-4.1, GPT-4o, o-series, GPT-4 Turbo and GPT-3.5) we use the exact tiktoken BPE encodings (o200k_base and cl100k_base), so counts match the API precisely. Anthropic, Google, Meta, DeepSeek, xAI and Mistral do not publish browser tokenizers, so those counts are close estimates based on calibrated character-to-token ratios.
Is my text sent to a server?
No. This tool runs 100% in your browser. Your prompts, documents and code never leave your device, are never uploaded, and are never logged. That makes it safe for confidential and proprietary content.
How do I estimate my API cost?
Your pasted text is treated as input tokens. Enter the number of tokens you expect the model to generate in the 'expected output' field, and the tool multiplies both by each model's per-million-token price to show a total cost. You can compare the same prompt across every model instantly.
Why do different models report different token counts?
Each model family is trained with its own tokenizer and vocabulary, so the same text splits into a different number of tokens. Newer vocabularies (like OpenAI's o200k_base) are generally more efficient than older ones, which is why GPT-4o often uses fewer tokens than GPT-3.5 for identical text.
Does counting tokens help me avoid context window errors?
Yes. Every model has a maximum context window (input + output). This tool shows how much of each model's window your prompt consumes, so you can trim or chunk content before you hit a limit and get a truncation or error.